Why does MCP exist?

An agent is only useful when it can act on real systems, and every system has its own API. MCP exists so that the work of exposing a system to an AI happens once and is reused everywhere, instead of being rewritten for every model, framework, and app.

The integration explosion #

Say you have M AI applications and N services you want them to use. Without a standard, you write up to M times N integrations, each coupled to a specific tool-calling format and a specific API. Add a model, redo the work for every service. Add a service, redo it for every model.

MCP collapses that to M plus N: each service gets one server, each application is one client, and they interoperate.

Separation of concerns #

The team that knows a service best (its auth, its rate limits, its edge cases) builds the server once. Application developers consume it without needing to learn the service internals. That division is why a healthy ecosystem of servers can grow independently of any one app.

Portability #

Because the protocol is uniform, the same server works in Claude Desktop, in an IDE, in a custom agent, or through an API connector. You are not locked into one runtime. This is the same reason Building effective agents treats a clean tool interface as foundational.

When MCP is not worth it #

A standard only pays off when you reuse it, and MCP is not free:

• Another moving part. A server is a process, or a network service, that can crash, hang, or drift out of date. It is one more thing to run, monitor, and secure.
• Latency and indirection. Calls hop through the client, and for remote servers over the network, which is slower than an in-process function call and adds a failure surface.
• A young spec and uneven servers. The protocol and ecosystem are still changing, transports and auth have already shifted between revisions, and third-party server quality varies widely.
• Operational cost when remote. Hosting a server means TLS, OAuth, rate limiting, and versioning, the same burden as any production API.

For a single application with a fixed set of tools you control, wiring the model's own function calling directly is simpler and faster. Reach for MCP when reuse across apps, models, or teams is the actual goal. See MCP vs plain function calling for that decision.